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How does the behavioral targeting actually work and how efficient is it?

The assumption of behavioral targeting (BT) is that if advertising better matches consumer interests, consumers are more likely to respond to the message and advertisers will be willing to pay more for ads delivered to such an audience.

A survey conducted by the Network Advertising Initiative (NAI) found out that:

1.    advertising rates are significantly higher when BT is used

2.    advertising using BT is more successful than standard RON advertising, creating greater utility for consumers and clear appeal for advertisers because of the increased conversion of ads into sales.

3.    a majority of network advertisers’ revenue is spent acquiring inventory, making BT an important source of revenue for publishers as well as ad networks[1]

Conventional media are able to obtain targeting data for their audiences through surveys and other tools such as registration data. This provides advertisers with a general idea of the audience thus enabling them to increase the chances of a successful advertising campaign.

After surveying 12 ad networks, including nine of the largest players in the market (based on the number of site visits), it was shown that BT is an essential part of an ad network, publisher, and advertiser success.

BT advertising is more effective with conversion rates more than double the rates for run of network advertising. Also click through rates (ad clicks divided by impressions delivered) are improved by as 670% over run of network advertising.

From a consumer perspective, this shows that such advertising is significantly more valuable because it is more likely to tell consumers about a product they want to buy.[2]

  1. Facebook: Facebook Ads; Sponsored Stories
  2. Twitter: Promoted Accounts, Promoted Tweets
  3. Google AdWords (for YouTube, Google+)

One step further in personalization is social indexing, a system used to collaboratively create and manage tags in order to annotate and categorize content. This practice is also known as collaborative tagging, social classification, folksonomy and social tagging.[3]

Social index can be more powerful than targeted content determined by user’s preferences because it mines friend’s “likes” following the assumption that friends “share” interests. A social index is created and used to refine the presented information. Then that information undergoes a like/dislike review and is filtered through friends’ social graphs to provide another level of content refinement. [4]

The concept was created in 2007 by Bret Taylor co-founder of FriendFeed – a social network acquired by Facebook in 2009. The “like” button became an important data collection tool when used together with Facebook’s user base, having approx.  600 million people at that time. Any site can add the “like” button to its pages. If a person clicks the “like” button, that links is automatically shared with his or her Facebook friends. At the same time, the button is fed into Taylor’s index.[5]

An example of using social indexing is the TripAdvisor’s Wisdom of Friends, a feature that allows travelers to receive advice on TripAdvisor from their Facebook friends. Moreover, the feature Friend of a Friend also allows users to see reviews and opinions from second-degree contacts. These features supplement the over 100 million reviews and opinions from travelers around the world and make the user experience highly personalized.[6]

Another example of the use of semantic technology is given by AvisBudgetGroup model. To understand more about travellers, the company uses information from hotel and flight bookings. Therefore, it can create a tailored offer. Avis Budget works with OpenTravel to deliver targeted offers based on reservation history.[7]

Facebook took personalised search one step further. Facebook Social Graph  was initially available only for a few US states, but was launched worldwide at the end on March 2013.

The old search on Facebook (called PPS) was keyword based, meaning that the user entered keywords and the search engine produced the results. A few years later, a new search product was launched, called Typeahead. It delivers search results as the searcher is typing, using “prefix matching.”

Graph Search intends to extend the current capability of search engines to also search based on the relationship between entities, using natural language as the input for the queries. For example:

  • Restaurants liked by Facebook employees
  • People who went to Austria
  • Restaurants in Paris liked by people who graduated from Vrije Universiteit, Brussels.[8]

Facebook Social Graph has the potential to optimize campaigns and pages so as to become more likely to appear in users’ search results. However, marketers should be aware of the risks that come with employees “liking“ pages that are against the brand’s mission (e.g. pages that promote racism).

[1] Beales, Howard, The Value of Behavioral Targeting (2010), study sponsored by the Network Advertising Initiative (NAI), http://www.networkadvertising.org/pdfs/Beales_NAI_Study.pdf Last consulted on 12 January 2013

[2] IDEM

[3] Wikipedia, Folksonomy, http://en.wikipedia.org/wiki/Folksonomy Last consulted on 12 January 2013

[4] WTM &PhoCusWright Month Report, Travel Innovation and Technology Trends:2012 and Beyond Social Media: The Cornerstone of Marketing Strategy

http://www.wtmlondon.com/page.cfm/Action=fileDownload/formatFor=library_2_assocPDF/fileName=7220668_assocPDF/fileExt=pdf Last  consulted on 12 January 2013

[5] Technology Review, Social Indexing, June 2011, http://www2.technologyreview.com/article/423688/social-indexing/ Last consulted on 12 January 2013

[6] TripAdvisor, Fact Sheet, http://www.tripadvisor.com/PressCenter-c4-Fact_Sheet.html Last consulted on 12 January 2013

[7]WTM &PhoCusWright’s, Travel Innovation and Technology Trends: 2012 and Beyond, Semantic Technology (Finally) Becomes Relevant

http://www.wtmlondon.com/page.cfm/Action=fileDownload/formatFor=library_2_assocPDF/fileName=8344824_assocPDF/fileExt=pdf Last consulted on 12 January 2013

[8] Facebook Engineering (Notes), Under the Hood: Building out the infrastructure for Graph Search, March 6, 2013

https://www.facebook.com/notes/facebook-engineering/under-the-hood-building-out-the-infrastructure-for-graph-search/10151347573598920 Last consulted on 12 April 2013


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